ML algorithms for the assessment of prescribed physical exercises

Villa, Sara García de; Parra, Andrea Martinez; Martín, Ana Jiménez; Domínguez, Juan Jesús García; Casillas-Pérez, David

doi:10.1109/memea52024.2021.9478725

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…A different method is through the application of various statistical/aggregate functions (e.g., minimum, mean, and standard deviation). This produces descriptive statistics for the timeseries, which are used as features [36]. These features are often used with shallow ML algorithms.…”

Section: B Feature Extraction Engineering and Selectionmentioning

confidence: 99%

“…RF was used in 10/17 publications using ensemble techniques [29], [46], [52], [60], [61], [73], [76], [79], [112], [113], and eXtreme Gradient Boosting (XGBoost) was used in three publications [34], [65], [104]. Support Vector Machines (SVM) were also widely used, with 14/51 publications applying them, for both classification [3], [21], [31], [32], [33], [36], [63], [88], [89], [90], [100], [108], [111] and regression [3], [47]. Other algorithms that were used include k-Nearest Neighbours (k-NN), which was used in four publications [71], [97], [105], [118], Logistic Regression used in three publications [48], [54], [59], and variations of Artificial Neural Networks (ANN) used in five publications [58], [87], [95], [98], [114].…”

Section: ) Model Trainingmentioning

confidence: 99%

“…10 shows the type of assessment used in the publications. Of the ones that used classification, most (21/88) used binary classification [36], [39], [49], [55], [59], [60], [61], [65], [66], [71], [72], [79], [84], [101], [102], [105], [106], [111], [114], [117], [120]; that is, datasets were assessed using a dichotomous variable (e.g., normal or abnormal motion). Other often-used types of assessments were using 3-point ordinal scale values evaluated from rank-based classification approaches [35], [48], [54], [62], [63], [64], [68], [77], [83], [86], [87], [97], [98], [99], [103], [107], [108], [109], and score-based assessments evaluated using regression methods [31], [40], [44], [45], [46],…”

Section: ) Model Trainingmentioning

confidence: 99%

“…For example, although ensemble techniques had high accuracies across most cases and configurations, their accuracy was lower when dealing with classification into multiple (i.e., more than 3-4) different categories [34], [52], [73], [76], [112], [113]. On the contrary, SVM algorithms were evaluated with higher accuracies when performing classification to dichotomous values (i.e., binary assessments) [32], [36], [66], [111].…”

Section: ) Model Validation and Testingmentioning

confidence: 99%

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Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Section: B Feature Extraction Engineering and Selectionmentioning

confidence: 99%

Section: ) Model Trainingmentioning

confidence: 99%

Section: ) Model Trainingmentioning

confidence: 99%

Section: ) Model Validation and Testingmentioning

confidence: 99%

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Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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“…De Villa et al . [ 26 ] used four IMUs to classify a set of seven exercises of upper and lower limbs frequently proposed in physical therapy routines (including squats, hip abduction, knee flex-extension, gait, elbow flex-extension, extension of arms overhead and squeezing), obtaining sensitivity and specificity values over 99% in the detection of wrongly performed motions using a Support Vector Machine (SVM) with a polynomial kernel. Depari et al .…”

Section: Introductionmentioning

confidence: 99%

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off

et al. 2023

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The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver. The aim of this work was to develop and validate a wearable IMU-based biofeedback system to monitor astronauts in-flight training displaying real-time feedback on exercises execution. Such a system has potential spin-offs also on personalized home/remote training for fitness and rehabilitation. 29 subjects were recruited according to their physical shape and performance criteria to collect kinematics data under ethical committee approval. Tests were conducted to (i) compare the signals acquired with our system to those obtained with the current state-of-the-art inertial sensors and (ii) to assess the exercises classification performance. The magnitude square coherence between the signals collected with the two different systems shows good agreement between the data. Multiple classification algorithms were tested and the best accuracy was obtained using a Multi-Layer Perceptron (MLP). MLP was also able to identify mixed errors during the exercise execution, a scenario that is quite common during training. The resulting system represents a novel low-cost training monitor tool that has space application, but also potential use on Earth for individuals working-out at home or remotely thanks to its ease of use and portability.

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Renaissance: A Smart Mobile Application Featuring Elderly People Care and Assistance System for the Bangladeshi Senior Citizens

Chowdhury

Ahmad

2023

2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS

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ML algorithms for the assessment of prescribed physical exercises

Cited by 5 publications

References 18 publications

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off

Renaissance: A Smart Mobile Application Featuring Elderly People Care and Assistance System for the Bangladeshi Senior Citizens

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